Implement cudf::reduce for decimal32 and decimal64 (part 2)
#6980
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## branch-0.18 #6980 +/- ##
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+ Coverage 82.04% 82.06% +0.02%
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Files 96 96
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davidwendt
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nvdbaranec
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| return std::make_unique<fixed_point_scalar<T>>(std::move(temp_data), | ||
| numeric::scale_type{input.type().scale()}, | ||
| temp_valid.value(stream), |
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Just wondering if it makes sense to add scalar constructor with rmm::device_scalar<bool>&& valid argument. (not in the scope of this PR)
std::move(temp_valid).
It would avoid extra DtoH and HtoD copy of single bool.
davidwendt
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rerun tests |
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This PR resolves a part of #3556.
Supporting
cudf::reduce:MIN,MAX,SUM&PRODUCT&NUNIQUE) Implementcudf::reducefordecimal32anddecimal64(part 1) #6814Reduction Ops:
Done in Previous PR
✔️
SUM, ///< sum reduction✔️
PRODUCT, ///< product reduction✔️
MIN, ///< min reduction✔️
MAX, ///< max reduction✔️
NUNIQUE, ///< count number of unique elementsNot supported by
cudf::reduce:COUNT_VALID, ///< count number of valid elementsCOUNT_ALL, ///< count number of elementsCOLLECT, ///< collect values into a listLEAD, ///< window function, accesses row at specified offset following current rowLAG, ///< window function, accesses row at specified offset preceding current rowPTX, ///< PTX UDF based reductionCUDA ///< CUDA UDf based reductionARGMAX, ///< Index of max elementARGMIN, ///< Index of min elementROW_NUMBER, ///< get row-number of elementWon't be supported:
ANY, ///< any reductionALL, ///< all reductionTo Do / Investigate:
SUM_OF_SQUARES, ///< sum of squares reductionMEDIAN, ///< median reductionQUANTILE, ///< compute specified quantile(s)NTH_ELEMENT, ///< get the nth elementDeferred until requested
MEAN, ///< arithmetic mean reductionVARIANCE, ///< groupwise varianceSTD, ///< groupwise standard deviation